#' Format MR results for a 1-to-many forest plot
#'
#' This function formats user-supplied results for the [forest_plot_1_to_many()] function. 
#' The user supplies their results in the form of a data frame. 
#' The data frame is assumed to contain at least three columns of data: 
#' \enumerate{
#' \item effect estimates, from an analysis of the effect of an exposure on an outcome; 
#' \item standard errors for the effect estimates; and 
#' \item a column of trait names, corresponding to the 'many' in a 1-to-many forest plot.
#' }
#' 
#' @param mr_res Data frame of results supplied by the user.
#' @param b Name of the column specifying the effect of the exposure on the outcome. Default = `"b"`.
#' @param se Name of the column specifying the standard error for b. Default = `"se"`.
#' @param TraitM The column specifying the names of the traits. Corresponds to 'many' in the 1-to-many forest plot. Default=`"outcome"`.
#' @param addcols Name of any additional columns to add to the plot. Character vector. The default is `NULL`.
#' @param by Name of the column indicating a grouping variable to stratify results on. Default=`NULL`.
#' @param exponentiate Convert log odds ratios to odds ratios? Default=`FALSE`.
#' @param ao_slc Logical; retrieve trait subcategory information using [available_outcomes()]. Default=`FALSE`.
#' @param weight The default is `NULL`.
#'
#' @export
#' @return data frame.
format_1_to_many <- function(mr_res, b="b",se="se",exponentiate=FALSE, ao_slc=FALSE,by=NULL,TraitM="outcome",addcols=NULL,weight=NULL)
{
	if(!is.null(by)){
		mr_res<-mr_res[,names(mr_res)!="subcategory"]
		names(mr_res)[names(mr_res)==by]<-"subcategory"
	}else{
		mr_res$subcategory<-""
	}

	if(is.null(weight)) {
		mr_res$weight=3
	}

	if(TraitM=="exposure"){ #the plot function currently tries to plot separate plots for each unique exposure. This is a legacy of the original multiple exposures forest plot function and needs to be cleaned up. The function won't work if the TraitM column is called exposure
		names(mr_res)[names(mr_res)=="exposure"]<-"TraitM"
		TraitM<-"TraitM"
	}

	names(mr_res)[names(mr_res)==b ]<-"b"
	names(mr_res)[names(mr_res)==se ]<-"se"
	Letters<-c("A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z")
	Letters<-sort(c(paste0("A",Letters),paste0("B",Letters),paste0("C",Letters),paste0("D",Letters)))
	mr_res$outcome2<-mr_res[,TraitM]
	mr_res[,TraitM]<-paste(Letters[1:length(mr_res[,TraitM])],mr_res[,TraitM])

	mr_res$subcategory<-trim(mr_res$subcategory)
	mr_res$exposure<-""

	# Get extra info on outcomes
	if(ao_slc) 
	{ 
		ao <- available_outcomes()
		ao$subcategory[ao$subcategory == "Cardiovascular"] <- "Cardiometabolic"
		ao$subcategory[ao$trait == "Type 2 diabetes"] <- "Cardiometabolic"
		names(ao)[names(ao) == "nsnp"]<-"nsnp.array"
	}

	dat<-mr_res
	dat$index <- 1:nrow(dat)
	
	if(ao_slc)
	{ 
		dat <- merge(dat, ao, by.x="id.outcome", by.y="id")
	}
	dat <- dat[order(dat$b), ]

	# Create CIs
	dat$up_ci <- as.numeric(dat$b) + 1.96 * as.numeric(dat$se)
	dat$lo_ci <- as.numeric(dat$b) - 1.96 * as.numeric(dat$se)

	# Exponentiate?
	if(exponentiate)
	{
		dat$b <- exp(as.numeric(dat$b))
		dat$up_ci <- exp(dat$up_ci)
		dat$lo_ci <- exp(dat$lo_ci)
	}
	
	# Organise cats
	dat$subcategory <- as.factor(dat$subcategory)
	
	if(!ao_slc) #generate a simple trait column. this contains only the outcome name (ie excludes consortium and year from the outcome column generated by mr()). This step caters to the possibility that a user's results contain a mixture of results obtained via MR-Base and correspondence. The later won't be present in the MR-Base database. However, still need to split the outcome name into trait, year and consortium. 
	{

		dat$trait<-as.character(dat[,TraitM])
		Pos<-grep("\\|\\|",dat$trait) #this indicates the outcome column was derived from data in MR-Base. Sometimes it wont look like this e.g. if the user has supplied their own outcomes
		if(sum(Pos)!=0)
		{
			Outcome<-dat$trait[Pos]
			Outcome<-unlist(strsplit(Outcome,split="\\|\\|"))
			Outcome<-Outcome[seq(1,length(Outcome),by=2)]
			Outcome<-trim(Outcome)
			dat$trait[Pos]<-Outcome
		}

	}


	dat1 <- data.frame(
		exposure = as.character(dat$exposure),
		outcome = as.character(dat$trait),
		outcome2= as.character(dat$outcome2),
		category = as.character(dat$subcategory),
		effect = dat$b,
		se = dat$se,
		up_ci = dat$up_ci,
		lo_ci = dat$lo_ci,
		index = dat$index,
		weight=dat$weight,
		stringsAsFactors = FALSE
	)

	if(!is.null(addcols)){
		dat2<-dat[,addcols]
		dat<-cbind(dat1,dat2)
		if(length(addcols)==1){
			names(dat)[names(dat)=="dat2"]<-addcols
		}
	}else{
		dat<-dat1
	}

	exps <- unique(dat$exposure)
	
	dat <- dat[order(dat$index), ]

	dat <- dat[order(dat$outcome), ]

	return(dat)
}

#' Sort results for 1-to-many forest plot
#'
#' This function sorts user-supplied results for the [forest_plot_1_to_many()] function. The user supplies their results in the form of a data frame.    
#' 
#' @param mr_res Data frame of results supplied by the user.
#' @param b Name of the column specifying the effect of the exposure on the outcome. The default is `"b"`.
#' @param trait_m The column specifying the names of the traits. Corresponds to 'many' in the 1-to-many forest plot. The default is `"outcome"`.
#' @param group Name of grouping variable in `mr_res`. 
#' @param priority If `sort_action = 3`, choose which value of the `trait_m` variable should be given priority and go above the other `trait_m` values. 
#' The trait with the largest effect size for the prioritised group will go to the top of the plot. 
#' @param sort_action Choose how to sort results. 
#' \itemize{
#' \item `sort_action = 1`: sort results by effect size within groups. Use the group order supplied by the user. 
#' \item `sort_action = 2`: sort results by effect size and group. Overides the group ordering supplied by the user. 
#' \item `sort_action = 3`: group results for the same trait together (e.g. multiple results for the same trait from different MR methods).
#' \item `sort_action = 4`: sort by decreasing effect size (largest effect size at top and smallest at bottom). 
#' \item `sort_action = 5`: sort by increasing effect size (smallest effect size at top and largest at bottom).
#' }
#'
#' @export
#' @return data frame.
#' 
sort_1_to_many <- function(mr_res,b="b",trait_m="outcome",sort_action=4,group=NULL,priority=NULL){

	mr_res[,trait_m]<-as.character(mr_res[,trait_m])
	mr_res[,group]<-as.character(mr_res[,group])
	if(!b %in% names(mr_res)) warning("Column with effect estimates not found. Did you forget to specify the column of data containing your effect estimates?")
	if(sort_action==1){
		if(is.null(group)) warning("You must indicate a grouping variable")
		
		# Numbers<-1:100
		Letters<-c("A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z")
		Letters<-sort(c(paste0("A",Letters),paste0("B",Letters),paste0("C",Letters)))
		groups<-unique(mr_res[,group])
		mr_res$Index<-unlist(lapply(1:length(unique(mr_res[,group])),FUN=function(x) rep(Letters[Letters==Letters[x]],length(which(mr_res[,group]==groups[x])))))
		mr_res<-mr_res[order(mr_res[,b],decreasing=TRUE),]
		mr_res$Index2<-Letters[1:nrow(mr_res)]
		mr_res$Index3<-paste(mr_res$Index,mr_res$Index2,sep="")
		mr_res<-mr_res[order(mr_res$Index3),]
		mr_res<-mr_res[,!names(mr_res) %in% c("Index","Index2","Index3")]
	}

	if(sort_action ==2){
		if(is.null(group)) warning("You must indicate a grouping variable")
		mr_res<-mr_res[order(mr_res[,b],decreasing=TRUE),]
		mr_res<-mr_res[order(mr_res[,group]),]
	}
		
	if(sort_action==3){
		if(is.null(group)) warning("You must indicate a grouping variable")
		if(is.null(priority)) warning("You must indicate which value of the grouping variable ",group," to use as the priority value")

		mr_res$b.sort<-NA
		mr_res1<-mr_res[mr_res[,group] %in% mr_res[,group][duplicated(mr_res[,group])],]
		mr_res2<-mr_res[!mr_res[,group] %in% mr_res[,group][duplicated(mr_res[,group])],]

		mr_res1$b.sort[mr_res1[,trait_m]==priority]<-mr_res1[,b][mr_res1[,trait_m]==priority]
		# mr_res1$b.sort[mr_res1[,group]==priority]<-1000
		for(i in unique(mr_res1[,group]))
		{
			mr_res1$b.sort[mr_res1[,group] == i & is.na(mr_res1$b.sort)]<-mr_res1$b.sort[mr_res1[,group]== i & !is.na(mr_res1$b.sort)]
		}
		# mr_res1$b.sort[is.na(mr_res1$b.sort)]<-mr_res1$b.sort[!is.na(mr_res1$b.sort)]
		mr_res2$b.sort<-mr_res2$b
		mr_res<-rbind(mr_res1,mr_res2)

		mr_res<-mr_res[order(mr_res$b.sort,decreasing=TRUE),]
		groups<-unique(mr_res[,group])
		List<-NULL
		for(i in 1:length(groups)){
			Test<-mr_res[mr_res[,group]==groups[i],]
			Test1<-Test[Test[,trait_m] != priority,]
			Test2<-Test[Test[,trait_m] == priority,]
			List[[i]]<-rbind(Test2,Test1)
		}
		mr_res<-do.call(rbind,List)
		
	}

	if(sort_action ==4){
		mr_res<-mr_res[order(mr_res[,b],decreasing=TRUE),]
	}

	if(sort_action ==5){
		mr_res<-mr_res[order(mr_res[,b],decreasing=FALSE),]
	}

	return(mr_res)
	
}

#' A basic forest plot
#'
#' This function is used to create a basic forest plot.
#' It requires the output from [format_1_to_many()].
#'
#' @param dat Output from [format_1_to_many()]
#' @param section Which category in dat to plot. If `NULL` then prints everything.
#' @param colour_group Which exposure to plot. If `NULL` then prints everything grouping by colour.
#' @param colour_group_first The default is `TRUE`.
#' @param xlab x-axis label. Default=`NULL`.
#' @param bottom Show x-axis? Default=`FALSE`.
#' @param trans x-axis scale.
#' @param xlim x-axis limits.
#' @param lo Lower limit of x axis.
#' @param up Upper limit of x axis.
#' @param subheading_size text size for the subheadings. The subheadings correspond to the values of the section argument.
#' @param colour_scheme the general colour scheme for the plot. Default is to make all text and data points `"black"`.
#' @param shape_points the shape of the data points to pass to [ggplot2::geom_point()]. Default is set to `15` (filled square).
#'
#' @return ggplot object
forest_plot_basic2 <- function(dat, section=NULL, colour_group=NULL, colour_group_first=TRUE, xlab=NULL, bottom=TRUE, trans="identity", xlim=NULL, lo=lo,up=up,subheading_size=subheading_size,colour_scheme="black",shape_points=15)
{
	if(bottom)
	{
		text_colour <- ggplot2::element_text(colour="black")
		tick_colour <- ggplot2::element_line(colour="black")
		xlabname <- xlab
	} else {
		text_colour <- ggplot2::element_blank()
		tick_colour <- ggplot2::element_blank()
		xlabname <- NULL
	}

	# OR or log(OR)?
	# If CI are symmetric then log(OR)
	# Use this to guess where to put the null line
	null_line <- ifelse(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect) == TRUE, 0, 1)

	# Change lab
	if(!is.null(xlim))
	{
		stopifnot(length(xlim) == 2)
		stopifnot(xlim[1] < xlim[2])
		dat$lo_ci <- pmax(dat$lo_ci, xlim[1], na.rm=TRUE)
		dat$up_ci <- pmin(dat$up_ci, xlim[2], na.rm=TRUE)
	}

	if(is.null(up) | is.null(lo) ){
		up <- max(dat$up_ci, na.rm=TRUE)
		lo <- min(dat$lo_ci, na.rm=TRUE)
	}
	r <- up-lo
	lo_orig <- lo
	lo <- lo - r * 0.5

	if(!is.null(section))
	{
		dat <- subset(dat, category==section)
		main_title <- section
	} else {
		main_title <- ""
	}

	if(!is.null(colour_group))
	{
		dat <- subset(dat, exposure == colour_group)
		point_plot <- ggplot2::geom_point(size=dat$weight,colour=colour_scheme,fill=colour_scheme,shape=shape_points)
	} else {
		point_plot <- ggplot2::geom_point(ggplot2::aes(colour=colour_scheme), size=dat$weight,fill=colour_scheme)
	}

	if((!is.null(colour_group) & colour_group_first) | is.null(colour_group))
	{
		outcome_labels <- ggplot2::geom_text(ggplot2::aes(label=outcome2,colour=colour_scheme), x=lo, y=mean(c(1, length(unique(dat$exposure)))), hjust=0, vjust=0.5, size=2.5)
		main_title <- ifelse(is.null(section), "", section)
		title_colour <- "black"

	} else {
		outcome_labels <- NULL
		lo <- lo_orig
		main_title <- ""
		title_colour <- "white"
	}

	main_title <- section

	dat$lab<-dat$outcome
	l <- data.frame(lab=sort(unique(dat$lab)), col="a", stringsAsFactors=FALSE)
	l$col[1:nrow(l) %% 2 == 0] <- "b"

	dat <- merge(dat, l, by="lab", all.x=TRUE)
	dat <- dat[nrow(dat):1, ]

	p <-ggplot2::ggplot(dat, ggplot2::aes(x=effect, y=exposure)) +
	ggplot2::geom_rect(ggplot2::aes(fill=col), colour=colour_scheme,xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) +
	ggplot2::geom_vline(xintercept=seq(ceiling(lo_orig), ceiling(up), by=0.5), alpha=0, size=0.3) +
	ggplot2::geom_vline(xintercept=null_line, colour="#333333", size=0.3) +
	# ggplot2::geom_errorbarh(ggplot2::aes(xmin=lo_ci, xmax=up_ci), height=0, size=0.4, colour="#aaaaaa") +
	ggplot2::geom_errorbarh(ggplot2::aes(xmin=lo_ci, xmax=up_ci), height=0, size=0.4, colour=colour_scheme) +
	# ggplot2::geom_point(colour="black", size=2.2) +
	ggplot2::geom_point(colour=colour_scheme, size=2.2,shape=shape_points,fill=colour_scheme) +
	# ggplot2::scale_fill_manual(values="cyan4")+
	point_plot +
	ggplot2::facet_grid(lab ~ .) +
	ggplot2::scale_x_continuous(trans=trans, limits=c(lo, up)) +
	ggplot2::scale_colour_brewer(type="qual") +
	# ggplot2::scale_fill_manual(values=c("#eeeeee", "#ffffff"), guide=FALSE) +
	ggplot2::scale_fill_manual(values=c("#eeeeee", "#ffffff"), guide=FALSE) +
	ggplot2::theme(
		axis.line=ggplot2::element_blank(),
		axis.text.y=ggplot2::element_blank(), 
		axis.ticks.y=ggplot2::element_blank(), 
		axis.text.x=text_colour, 
		axis.ticks.x=tick_colour, 
		# strip.text.y=ggplot2::element_text(angle=360, hjust=0), 
		strip.background=ggplot2::element_rect(fill="white", colour="white"),
		strip.text=ggplot2::element_text(family="Courier New", face="bold", size=9),
		legend.position="none",
		legend.direction="vertical",
		panel.grid.minor.x=ggplot2::element_blank(),
		panel.grid.minor.y=ggplot2::element_blank(),
		panel.grid.major.y=ggplot2::element_blank(),
		plot.title = ggplot2::element_text(hjust = 0, size=subheading_size, colour=title_colour),
		plot.margin=ggplot2::unit(c(2,3,2,0), units="points"),
		plot.background=ggplot2::element_rect(fill="white"),
		panel.spacing=ggplot2::unit(0,"lines"),
		panel.background=ggplot2::element_rect(colour="white", fill=colour_scheme, size=1),
		strip.text.y = ggplot2::element_blank()
		# strip.background = ggplot2::element_blank()
	) +
	ggplot2::labs(y=NULL, x=xlabname, colour="", fill=NULL, title=main_title) +
	outcome_labels
	return(p)
}


forest_plot_names2 <- function(dat, section=NULL, var1="outcome2",bottom=TRUE,title="",subheading_size=subheading_size,colour_scheme="black",shape_points=15,col_text_size=5)
{
	if(bottom)
	{
		text_colour <- ggplot2::element_text(colour="white")
		tick_colour <- ggplot2::element_line(colour="white")
		xlabname <- ""
	} else {
		text_colour <- ggplot2::element_blank()
		tick_colour <- ggplot2::element_blank()
		xlabname <- NULL
	}

	# OR or log(OR)?
	# If CI are symmetric then log(OR)
	# Use this to guess where to put the null line
	null_line <- ifelse(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect) == TRUE, 0, 1)

	# up <- max(dat$up_ci, na.rm=TRUE)
	# lo <- min(dat$lo_ci, na.rm=TRUE)
	# r <- up-lo
	# lo_orig <- lo
	# lo <- lo - r * 0.5
	lo <- 0
	up <- 1

	if(!is.null(section))
	{
		dat <- subset(dat, category==section)
		main_title <- section
		section_colour <- "black"
	} else {
		main_title <- section
		section_colour <- "white"
	}

	point_plot <- ggplot2::geom_point(ggplot2::aes(colour=exposure), size=2)

	outcome_labels <- ggplot2::geom_text(
		ggplot2::aes(label=eval(parse(text=var1))), 
		x=lo, 
		y=mean(c(1, length(unique(dat$exposure)))), 
		hjust=0, vjust=0.5, size=col_text_size,color=colour_scheme
	)

	# print(paste0("title=",title))
	if(section=="")	main_title <- title
	

	dat$lab<-dat$outcome
	l <- data.frame(lab=sort(unique(dat$lab)), col="a", stringsAsFactors=FALSE)

	l$col[1:nrow(l) %% 2 == 0] <- "b"

	dat <- merge(dat, l, by="lab", all.x=TRUE)

	p <- ggplot2::ggplot(dat, ggplot2::aes(x=effect, y=exposure)) +
	ggplot2::geom_rect(ggplot2::aes(fill=col),colour=colour_scheme, xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) +
	ggplot2::facet_grid(lab ~ .) +
	ggplot2::scale_x_continuous(limits=c(lo, up)) +
	ggplot2::scale_colour_brewer(type="qual") +
	ggplot2::scale_fill_manual(values=c("#eeeeee", "#ffffff"), guide=FALSE) +
	ggplot2::theme(
		axis.line=ggplot2::element_blank(),
		axis.text.y=ggplot2::element_blank(), 
		axis.ticks.y=ggplot2::element_blank(), 
		axis.text.x=text_colour, 
		axis.ticks.x=tick_colour, 
		# strip.text.y=ggplot2::element_text(angle=360, hjust=0), 
		strip.background=ggplot2::element_rect(fill="white", colour="white"),
		strip.text=ggplot2::element_text(family="Courier New", face="bold", size=11),
		legend.position="none",
		legend.direction="vertical",
		panel.grid.minor.x=ggplot2::element_blank(),
		panel.grid.minor.y=ggplot2::element_blank(),
		panel.grid.major.y=ggplot2::element_blank(),
		plot.title = ggplot2::element_text(hjust = 0, size=subheading_size, colour=section_colour),
		plot.margin=ggplot2::unit(c(2,0,2,0), units="points"),
		plot.background=ggplot2::element_rect(fill="white"),
		panel.spacing=ggplot2::unit(0,"lines"),
		panel.background=ggplot2::element_rect(colour=colour_scheme, fill=colour_scheme, size=1),
		strip.text.y = ggplot2::element_blank()
		# strip.background = ggplot2::element_blank()
	) +
	ggplot2::labs(y=NULL, x=xlabname, colour="", fill=NULL, title=main_title) +
	outcome_labels
	return(p)
}


forest_plot_addcol <- function(dat, section=NULL, addcol=NULL,bottom=TRUE,addcol_title=NULL,subheading_size=subheading_size,colour_scheme="black",shape_points=15,col_text_size=5)
{
	print(addcol)
	# print(addcol_title)
	if(bottom)
	{
		text_colour <- ggplot2::element_text(colour="white")
		tick_colour <- ggplot2::element_line(colour="white")
		xlabname <- ""
	} else {
		text_colour <- ggplot2::element_blank()
		tick_colour <- ggplot2::element_blank()
		xlabname <- NULL
	}

	# OR or log(OR)?
	# If CI are symmetric then log(OR)
	# Use this to guess where to put the null line
	null_line <- ifelse(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect) == TRUE, 0, 1)

	lo <- 0
	up <- 1

	if(!is.null(section))
	{
		dat <- subset(dat, category==section)
		main_title <- section
		section_colour <- "black"
	} else {
		main_title <- section
		section_colour <- "white"
	}

	point_plot <- ggplot2::geom_point(ggplot2::aes(colour=exposure), size=2)

	outcome_labels <- ggplot2::geom_text(
		ggplot2::aes(label=eval(parse(text=addcol))), 
		x=lo,
		y=mean(c(1, length(unique(dat$exposure)))), 
		hjust=0, vjust=0.5, size=col_text_size,colour=colour_scheme
	)

	main_title <- section

	dat$lab<-dat$outcome
	l <- data.frame(lab=sort(unique(dat$lab)), col="a", stringsAsFactors=FALSE)
	l$col[1:nrow(l) %% 2 == 0] <- "b"

	dat <- merge(dat, l, by="lab", all.x=TRUE)

	p <- ggplot2::ggplot(dat, ggplot2::aes(x=effect, y=exposure)) +
	ggplot2::geom_rect(ggplot2::aes(fill=col),colour=colour_scheme ,xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) +
	ggplot2::facet_grid(lab ~ .) +
	ggplot2::scale_x_continuous(limits=c(lo, up)) +
	ggplot2::scale_colour_brewer(type="qual") +
	ggplot2::scale_fill_manual(values=c("#eeeeee", "#ffffff"), guide=FALSE) +
	ggplot2::theme(
		axis.line=ggplot2::element_blank(),
		axis.text.y=ggplot2::element_blank(), 
		axis.ticks.y=ggplot2::element_blank(), 
		axis.text.x=text_colour, 
		axis.ticks.x=tick_colour, 
		# strip.text.y=ggplot2::element_text(angle=360, hjust=0), 
		strip.background=ggplot2::element_rect(fill="white", colour="white"),
		strip.text=ggplot2::element_text(family="Courier New", face="bold", size=11),
		legend.position="none",
		legend.direction="vertical",
		panel.grid.minor.x=ggplot2::element_blank(),
		panel.grid.minor.y=ggplot2::element_blank(),
		panel.grid.major.y=ggplot2::element_blank(),
		plot.title = ggplot2::element_text(hjust = 0, size=subheading_size, colour=section_colour),
		plot.margin=ggplot2::unit(c(2,0,2,0), units="points"),
		plot.background=ggplot2::element_rect(fill="white"),
		panel.spacing=ggplot2::unit(0,"lines"),
		panel.background=ggplot2::element_rect(colour="red", fill=colour_scheme, size=1),
		strip.text.y = ggplot2::element_blank(),
		strip.text.x = ggplot2::element_blank()
		# strip.background = ggplot2::element_blank()
	) +
	ggplot2::labs(y=NULL, x=xlabname, colour="", fill=NULL, title=addcol_title) +
	outcome_labels
	return(p)
}

#' 1-to-many forest plot 
#'
#' Plot results from an analysis of multiple exposures against a single outcome or a single exposure against multiple outcomes.
#' Plots effect estimates and 95 percent confidence intervals.
#' The ordering of results in the plot is determined by the order supplied by the user.
#' Users may find [sort_1_to_many()] helpful for sorting their results prior to using the 1-to-many forest plot. The plot function works best for 50 results and is not designed to handle more than 100 results. 
#' 
#' @param mr_res Data frame of results supplied by the user. The default is `"mr_res"`.
#' @param b Name of the column specifying the effect of the exposure on the outcome. The default is `"b"`.
#' @param se Name of the column specifying the standard error for b. The default is `"se"`.
#' @param TraitM The column specifying the names of the traits. Corresponds to 'many' in the 1-to-many forest plot. The default is `"outcome"`.
#' @param col1_title Title for the column specified by the TraitM argument. The default is `""`.
#' @param col1_width Width of Y axis label for the column specified by the TraitM argument. The default is `1`.
#' @param addcols Name of additional columns to plot. Character vector. The default is `NULL`.
#' @param addcol_titles Titles of additional columns specified by the addcols argument. Character vector. The default is `NULL`.
#' @param addcol_widths Widths of Y axis labels for additional columns specified by the addcols argument. Numeric vector. The default is `NULL`.
#' @param xlab X-axis label, default is `"Effect (95% confidence interval)"`.
#' @param by Name of the grouping variable to stratify results on. Default is `NULL`.
#' @param subheading_size text size for the subheadings specified in by argument. The default is `6`.
#' @param exponentiate Convert log odds ratios to odds ratios? Default is `FALSE`.
#' @param ao_slc Logical; retrieve trait subcategory information using available_outcomes(). Default is `FALSE`.
#' @param trans Specify x-axis scale. e.g. "identity", "log2", etc. If set to "identity" an additive scale is used. If set to log2 the x-axis is plotted on a multiplicative / doubling scale (preferable when plotting odds ratios). Default is `"identity"`.
#' @param lo Lower limit of X axis to plot. 
#' @param up upper limit of X axis to plot. 
#' @param colour_scheme the general colour scheme for the plot. Default is to make all text and data points `"black"`. 
#' @param shape_points the shape of the data points to pass to [ggplot2::geom_point()]. Default is set to `15` (filled square).
#' @param col_text_size The default is `5`.
#' @param weight The default is `NULL`.
#'
#' @export
#' @return grid plot object
#' 
forest_plot_1_to_many <- function(mr_res="mr_res", b="b",se="se",TraitM="outcome",col1_width=1,col1_title="",exponentiate=FALSE, trans="identity",ao_slc=TRUE,lo=NULL,up=NULL,by=NULL,xlab="Effect (95% confidence interval)",addcols=NULL,addcol_widths=NULL,addcol_titles="",subheading_size=6,shape_points=15,colour_scheme="black",col_text_size=5,weight=NULL){
	# if(is.null(lo) | is.null(up)) warning("Values missing for the lower or upper bounds of the x axis. Did you forget to set the lo and up arguments?")
	
	xlim=NULL
	ncols=1+length(addcols)
	if(addcol_titles==""){
		addcol_titles<-rep(addcol_titles,length(addcols))
	}
	
	dat <- format_1_to_many(
		mr_res=mr_res, 
		b=b,
		se=se,
		exponentiate=exponentiate, 
		ao_slc=ao_slc,
		by=by,
		TraitM=TraitM,
		addcols=addcols,
		weight=weight
	)
	

	legend <- cowplot::get_legend(
		ggplot2::ggplot(dat, ggplot2::aes(x=effect, y=outcome)) + 
		ggplot2::geom_point(ggplot2::aes(colour=exposure)) + 
		ggplot2::scale_colour_brewer(type="qual") + 
		ggplot2::labs(colour="Exposure") + 
		ggplot2::theme(text=ggplot2::element_text(size=10))
	)

	# message("howzit, may all your scripts be up-to-date and well annotated")
	if(length(addcols) != length(addcol_widths)) warning("length of addcols not equal to length of addcol_widths")
	sec <- unique(as.character(dat$category))
	columns <- unique(dat$exposure)
	l <- list()
	h <- rep(0, length(sec))
	count <- 1
	for(i in 1:length(sec))
	{
		h[i] <- length(unique(subset(dat, category==sec[i])$outcome))

		l[[count]] <- forest_plot_names2(
			dat, 
			sec[i],
			bottom = i==length(sec),
			title=col1_title,
			subheading_size=subheading_size,
			colour_scheme=colour_scheme,
			shape_points=shape_points,
			col_text_size=col_text_size
		)

		count <- count + 1

		if(!is.null(addcols)){

			for(j in 1:length(addcols)){
					l[[count]]<-forest_plot_addcol(
					dat,
					sec[i],
					addcol=addcols[j],
					addcol_title=addcol_titles[j],
					bottom = i==length(sec),
					subheading_size=subheading_size,
					colour_scheme=colour_scheme,
					shape_points=shape_points,
					col_text_size=col_text_size
				)

				count <- count + 1
			}
		}


		for(j in 1:length(columns))
		{
			l[[count]] <- forest_plot_basic2(
				dat, 
				sec[i], 
				bottom = i==length(sec), 
				colour_group=columns[j], 
				colour_group_first = FALSE, 
				xlab = paste0(xlab, " ", columns[j]), 
				lo=lo,
				up=up,
				trans = trans,
				xlim = xlim,
				subheading_size=subheading_size,
				colour_scheme=colour_scheme,
				shape_points=shape_points
			)
			count <- count + 1
		}
	}
	h <- h + 5
	h[length(sec)] <- h[length(sec)] + 1
	return(
		cowplot::plot_grid(
			gridExtra::arrangeGrob(
				grobs=l, 
				ncol=length(columns) + ncols, 
				nrow=length(h), 
				heights=h,
				widths=c(col1_width,addcol_widths, rep(5, length(columns)))
				
			)
		)
	)

}
